Abstract

Moment invariants have proven to be a useful tool for the detection of patterns in scalar and vector fields. By their
means, an interesting feature can be detected in a data set independent of its exact orientation, position, and scale.
In this paper, we show that they can also be applied to explore an unknown dataset without prior determination
of a query feature pattern it may possibly contain. The clustering of the high dimensional moment space reveals
the major structures in the underlying flow field and gives an excellent overview for subsequent more profound
exploration.